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CLC number: TP301.6; U11; F406.2
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-10-29
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Solving composite scheduling problems using the hybrid genetic algorithm
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Azuma Okamoto, Mitsumasa Sugawara. Solving composite scheduling problems using the hybrid genetic algorithm[J]. Journal of Zhejiang University Science A, 2010, 11(12): 953-958.
@article{title="Solving composite scheduling problems using the hybrid genetic algorithm",
author="Azuma Okamoto, Mitsumasa Sugawara",
journal="Journal of Zhejiang University Science A",
volume="11",
number="12",
pages="953-958",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1001136"
}
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%T Solving composite scheduling problems using the hybrid genetic algorithm
%A Azuma Okamoto
%A Mitsumasa Sugawara
%J Journal of Zhejiang University SCIENCE A
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%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1001136
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T1 - Solving composite scheduling problems using the hybrid genetic algorithm
A1 - Azuma Okamoto
A1 - Mitsumasa Sugawara
J0 - Journal of Zhejiang University Science A
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SP - 953
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%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1001136
Abstract: This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems. Two scheduling models were formulated as the elements of the composite scheduling model, and the composite model was formulated composing these models with indispensable additional constraints. A hybrid genetic algorithm was developed to solve the composite scheduling problems. An improved representation based on random keys was developed to search permutation space. A genetic algorithm based dynamic programming approach was applied to select resource. The proposed technique and a previous technique are compared by three types of problems. All results indicate that the proposed technique is superior to the previous one.
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[4]Okamoto, A., Gen, M., Sugawara, M., 2006a. Integrated data structure and scheduling approach for manufacturing and transportation using hybrid multistage operation-based genetic algorithm. Journal of Intelligent Manufacturing, 17(4):411-421.
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